As hydrocarbon reservoirs are easily harvested, including those oil reservoirs on land or located in shallow ocean water, are depleted, other hydrocarbon sources must be employed to meet energy demands. Such reservoirs may include any number of unconventional hydrocarbon sources, such as biomass, deep-water oil reservoirs, and natural gas from other sources. One such unconventional hydrocarbon source is natural gas produced from shale, termed “shale gas.”
The access and extraction of unconventional resources require the development of advanced technologies such as, for example, the process of hydraulic fracturing in low-permeability rocks, as it is the case for shale gas bearing rocks. In turn the development of novel technologies require an accurate knowledge of rock mechanical properties, say Poisson's ratio, bulk and shear moduli, etc. Currently quantitative analysis of rock mechanical properties are typically obtained through correlations (which are field dependent) and/or input from subject matter experts; however such a quantitative process is mediocre at best (with concurrent impact of the predictive capabilities of, say, hydraulic fracturing modeling).
In order to improve the quality of predictions of rock mechanical properties a suite of advanced rock physics models have been developed over the past two decades. These advanced rock physics models are based on first-principles and are therefore complex to solve for the rock mechanical properties of interest.
Current quantitative characterization of rock mechanical properties are mostly based on field dependent correlations combined with data obtained from logging tools. When rock physics models are actually used (in association with data from logging tools), the actual solution of the inverse problem associated with these models is done by the practitioner (geophysicist or engineer) by manually adjusting a host of physical parameters; once the difference between known data (obtained from the logging tools) and the predicted data is deemed to be in approximate agreement, the parameters are used once more into the rock physics model to obtain the rock mechanical properties (which are the quantities of interest).
The current approach has serious flaws; first it assumes that the inverse problem mentioned above has been solved appropriately by simple visual comparison between known data (from logging data) and predicted data (that obtained from the forward model); second it requires a significant expertise from the practitioner (therefore limiting its range of application); third it is not accurate (sometimes grossly inaccurate). The accuracy can, and usually has, a significant impact on inferred rock mechanical properties and, in turn, can seriously affect, through a non-linear, amplifying feedback loop, the predictive capabilities of novel advanced technologies (hydraulic fracturing, for example).
Despite these advances, there exists a need to address problems the aforementioned problems and issues. Therefore, what is needed is an improved computer-implemented method for automated rock physics modeling.